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In this paper, we demonstrate the value of 1H NMR
relaxometry for studying the hydration of clinker-reduced, more
climate-friendly cementitious binders. The results were obtained
on typical CEM I cements and samples containing two different
reactive agricultural ashes as well as non-reactive biochars as
supplementary cementitious materials. The findings prove that
time-resolved NMR measurements provide valuable additional
information when combined with classical heat flow calorimetry.
This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s).
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
Nuclear magnetic resonance (NMR) with focus on 1H protons is increasingly applied for non-destructive testing applications. Besides mobile NMR, laboratory devices such as the NMR core-analyzing tomograph are used. As their magnetic field is more homogeneous, they enable measurements with higher signal-to-noise ratios (SNR), but with limited sample sizes. The tomograph presented here (8.9 MHz) was constructed for a maximum sample diameter of 70 mm and length of up to 1 m. The resolution, the echo time (min. 50 µs), the SNR and the measurement type can be adjusted by means of exchangable coils. The tomograph enables measurements along the complete sensitive length, slice-selective and even 2- or 3-dimensional measurements. A movable sample lifting system thereby allows a precise positioning of the sample.
Vorstellung der Themen der Nachwuchsgruppe "Materialcharakterisierung und -informatik für die Nachhaltigkeit im Bauwesen" von Prof. Sabine Kruschwitz (TU Berlin und BAM)
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
Capillary active interior insulation materials are an important approach to minimize energy losses of historical buildings. A key factor for their performance is a high liquid conductivity, which enables redistribution of liquid moisture within the material. We set up an experiment to investigate the development of moisture profiles within two different interior insulation materials, calcium-silicate (CaSi) and expanded perlite (EP), under constant boundary conditions. The moisture profiles were determined by two different methods: simple destructive sample slicing with subsequent thermogravimetric drying as well as non-destructive NMR measurements with high spatial resolution. The moisture profiles obtained from both methods show good agreement, when compared at the low spatial resolution of sample slicing, which demonstrates the reliability of this method. Moreover, the measured T2-relaxation-time distributions across the sample depth were measured, which may give further insight into the saturation degree of the different pore sizes. In order to explain differences in the moisture profiles between CaSi and EP, we determined their pore-size distribution with different methods: conversion of the NMR T2 relaxationtime distribution at full saturation, mercury intrusion porosimetry and indirect determination from pressure plate measurements. CaSi shows a unimodal distribution at small pore diameters, while in EP, a bi-modal or wider distribution was found. We assume that the smaller pore diameters of CaSi lead to a higher capillary conductivity, which causes a more distributed moisture profile in comparison with that of EP.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
Relaxation phenomena observed in the electrical low-frequency range (approximately 1 mHz-10 kHz) of natural porous media like sandstones is often assumed to be directly related to the dominant (modal) pore throat sizes measured, for instance, with mercury intrusion porosimetry. Attempts to establish a universally valid relationship between pore size and peak Spectral Induced Polarization (SIP) relaxation time have failed, considering sandstones from very different origins and featuring great variations in textural and chemical compositions as well as in geometrical pore space properties. In addition working with characteristic relaxation times determined in Cole-Cole or Debye decomposition fits to build the relationship have not been successful. In particular, samples with narrow pore throats are often characterized by long SIP relaxation times corresponding to long “characteristic length scales” in these media, assuming that the diffusion coefficients along the electrical double layer were constant. Based on these observations, three different types of SIP relaxation can be distinguished. We present a new way of assessing complex pore spaces of very different sandstones in a multi-methodical approach to combine the benefits of mercury intrusion porosimetry, micro-computed tomography, and nuclear magnetic resonance. In this way, we achieve much deeper insight into the pore space due to the different resolutions and sensitivities of the applied methods to both pore constrictions (throats) and wide pores (pore bodies). We experimentally quantify pore aspect ratios and volume distributions within the two pore regions. We clearly observe systematic differences between three SIP relaxation types identified previously and can attribute the SIP peak relaxation times to measured characteristic length scales within our materials. We highlight selected results for a total of nine sandstones. It seems that SIP relaxation behavior depends on the size difference of the narrow pore throats to the wide pore bodies, which increases from SIP Type 1 to Type 3.
Integration of fibre reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel fibre reinforced concrete (FRC) is the deceleration of crack growth and hence it’s improved sustainability due to e.g. decrease of permeability of concrete by aggressive substances. Additional benefits are associated with the structural properties of FRC, where fibres can significantly increase the ductility and the tensile strength of concrete. In some applications, such as tunnel linings or industrial slabs, it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits. Fibre reinforcement can, however, have critical disadvantages and even hinder the performance of concrete, since it can induce an anisotropic material behaviour of the mixture if the fibres are not appropriately oriented. For a safe use of FRC in the future, reliable non-destructive methods need to be identified to assess the fibres’ orientation in hardened concrete. In this study, ultrasonic material testing, electrical impedance testing, and X-ray computer tomography have been investigated for this purpose using specially produced samples with biased or random fibre orientations. This paper demonstrates the capabilities of each of these NDT techniques for fibre orientation measurements and draws conclusions based on these results about the most promising areas for future research and development using these techniques.
Nuclear magnetic resonance (NMR) is a well established laboratory / borehole method to characterize the storage and transport properties of rocks due to its direct sensitivity to the corresponding pore fluid saturation (water or oil) and pore sizes.
For petrophysical applications there are several different NMR laboratory devices commercially available varying over a wide range of e.g. magnetic field strength / frequency (2 MHz to 30 MHz), applicable measurement protocols (T1, T2, T1-T2, T2-D, etc.) and sample sizes (2.5 cm to 10 cm in diameter). In this work we present NMR measurements, layed out in a round robin like manner, on a set of 20 sandstone samples. We use three different NMR devices containing two standard setups with homogenous magnetic fields (LIAG and RWTH) and one single-sided setup with gradient field (BGR) to measure T1 and T2 relaxation data. In our evaluation we especially focus on the comparison of the individually inverted relaxation time distributions to quantify the differences arising from different laboratory setups. Diverging results can be deduced on the one hand to the inherit differences between homogeneous and gradient fields but on the other hand also due to quality differences between the two homogeneous setups. Additionally, we also examine the influence of the individually chosen inversion parameters (signal processing, distribution sampling points, error weighting, regularization, etc.) to establish a general standardized best practice recommendation for future petrophysical NMR laboratory measurements.
The ability to reliably predict the amount and type of salts within a wet masonry is one of the most important and challenging subjects in the field of non-destructive combat of salt attack and preserving buildings and monuments of historical or archaeological value. To investigate the potential value of complex resistivity (CR) measurements for the early detection of salt and moisture related stone altering, a controlled salt type and concentration experiment series has been performed and a new procedure of an image based effective impedance modelling developed. Complex resistivity magnitude and phase measurements in the frequency range of 1 mHz to 100 Hz were acquired on a wide variety of wholly and partially brine saturated building stone samples. Deteriorating agents NaCl, Na2SO4, CaCl2 and MgSO4 were used. Since water and salt are not measured directly, the method is incapable of deducing possible building damages lacking a priori information. In the case of saturated materials, and provided thorough calibration data are available, a reliable estimate of salt concentration is possible from the measured resistivity magnitude. Additional indication on the dominant cation in the solution can be obtained from the measured resistivity phase, which is significantly higher the lower its valency. Furthermore, the measurements reported in this study give an important guide to the limitations of CR in obtaining pore surface area and pore throat estimates. For wet porous materials, in which polarization occurs due to complex surface conduction, the dominant pore throat and amount of specific surface affect its polarizability. Sandstones, sand-limestones and aerated concretes are more qualified observation objects (pore throats between 20 and 100 μm), whereas for bricks (pore sizes often < 5 μm) the method seems far less favorable. For those materials, which exhibit a Cole-Cole (C-C) type of relaxation, the phase peak is observed to decrease significantly with pore throat size and to occur at higher frequencies. The predicted power-law correlation between the C-C relaxation time and characteristic length scale (pore throat size for consolidated materials) is supported by the presented data. The experimental salinity study reveals how responsive polarization (in terms of imaginary conductivity) is to changing the ionic concentration or composition of the pore fluid. The properties of the electrical double layer and particularly its chemical composition are most likely the crucial controlling factors. The imaginary conductivity is observed to increase for most materials up to fluid salinities of about 1 S/m - a fact, that may be attributed to a mechanism of ion saturation within the electrical double layer; further ion supply seems to counteract this leading to ion-ion interactions, which decrease ionic mobility. CR measurements on partially saturated samples demonstrate the method’s sensitivity to water content. For most materials the imaginary conductivity component decreased significantly faster than the real component. Independent of the pore fluid and even though its salinity naturally increased during the evaporative drying procedure, the normalized saturation exponent of the quadrature component was about twice as high as the real one especially for clay-rich sandstones. The results revealed diverse behavior such as decreases and increases in relaxation time with saturation. In some cases a suppression of a distinctive relaxation curve at low saturations was observed. The results indicate, that contrary to conclusions from recent related studies, the correlation between a C-C relaxation time and hydraulic properties may be limited. In order to infer information on the charge distributions within the EDL, zeta-potentials and surface charge densities were obtained from electroacoustic measurements on particle suspension containing the same amounts and types of salt like used in the brine saturation CR study. The results add weight to the assumption that there is some universal positive relationship between zeta-potential and imaginary conductivity. A notable dependence of imaginary conductivity on diffuse layer surface charge was only observed in case of one sandstone (Cottaer), this being the material with the most abundant clay content. An image based effective impedance modelling approach revealed the fact that, even though it is not able to factor scale effects in, it is helpful to study general microstructural implications on CR responses. It was observed, that an increasing salt concentration (that creates a shrinking electrical double layer) reduces the peak phase and moves it towards higher frequency. The same effect would have an increasing fluid conductivity for a otherwise fixed microstructure. For some combinations of material and salt, however, in the experimental work the peak phase was observed to shift towards lower frequency: a phenomenon that could not be explained with the modelling. Therefore, it is assumed that chemical properties, like ion mobilities or other surface chemistry properties (ionexchange processes) must be regarded to conclusively explain surface conductivity mechanisms. Other experimental observations like the alteration of CR spectra at desaturation could likewise be modelled. If these models truly mimic the effective electrical properties, the results give new implications on the effective medium behavior. Concluding, the author values the complex resistivity method as a possible effective non-destructive testing (NDT) tool for a wide range of building stones. Depending on pore size and saturation important additional information can be obtained. In all cases, a priori information and calibration data are essential, that is, CR should not be treated as a stand-alone method. Further measurements are needed to develop a more complete model of the electrical double layer and its alteration with changing salinity and ion types.